Enhancing Severe Neutropenia Prediction: PKPD-Informed Labeling for Machine Learning Models Trained on Real-World Data.

ABSTRACT: Accurately labeling outcomes in real-world data for machine learning is challenging due to data sparsity and imbalances. This study developed and evaluated a pharmacokinetic-pharmacodynamic (PKPD)-informed labeling strategy to enhance the risk prediction of docetaxel-induced neutropenia. Machine learning models were trained on real-world data from 4,248 patients using two approaches for comparison. The "naive" labeling method used only neutrophil observations, while the "PKPD-informed" method used simulations from a semi-mechanistic model to determine the neutrophil nadir for each treatment cycle. Three machine learning models (logistic regression, XGBoost, TabPFN) were trained with baseline laboratory data to predict severe neutropenia (neutrophil count <0.1 cells × 10⁹/L) prio

SUBMITTER: O'Hanlon CJ 

PROVIDER: S-EPMC12816420 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature